Enhanced Data Processing Techniques with Pandas

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📅 2023-01-05 — Session: Enhanced Data Processing Techniques with Pandas

🕒 19:35–20:05
🏷️ Labels: Pandas, Data Processing, Python, Code Optimization, CSV
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to explore and implement advanced data processing techniques using Pandas in Python, focusing on handling data import errors, optimizing data processing workflows, and improving code readability.

Key Activities

  • Data Import Error Handling: Utilized error_bad_lines, usecols, and dtype parameters in Pandas to manage errors during data import and specify column data types.
  • CSV Error Management: Discussed strategies for ignoring problematic lines in CSV files and selecting columns post-import to maintain data integrity.
  • Data Processing Workflow: Developed a workflow to process columns, calculate medians, counts, and cumulative sums, and save results to a CSV file.
  • Quartile Analysis: Implemented code to calculate quartiles for DataFrame columns, merge data, and export results.
  • CSV File Merging: Merged multiple CSV files with additional data, consolidating them into a single DataFrame.
  • Code Optimization: Explored strategies for optimizing DataFrame processing by reducing redundant file reads and using dictionaries.
  • DHS Data Processing: Modified code to filter and process DHS data files, focusing on merging datasets based on specific columns.
  • Code Readability Improvement: Enhanced code readability for merging and concatenating data using Pandas.

Achievements

  • Successfully implemented techniques to handle data import errors and optimize data processing workflows.
  • Improved code readability and efficiency in data processing tasks.

Pending Tasks

  • Further exploration of optimization strategies for large datasets.
  • Implementation of additional error handling mechanisms for diverse data sources.

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